Analyzing the Role of Community and Individual Factors in LAMP Grant Funding: Identifying Potential Barriers Across US Counties

Author

Elliot Hohn
Sr. Agricultural Data Scientist Impact Fellow
Federation of American Scientists
ehohn@fas.org

May 2024

Executive Summary

This report investigates community characteristics influencing the allocation of USDA’s Local Agriculture Market Program (LAMP) grant funding. LAMP supports local food systems through grants to promote producer-to-consumer marketing, food enterprises, and value-added products. The analysis includes data on socioeconomic and environmental factors, using Principal Component Analysis (PCA), clustering, and regression analysis to identify patterns and key variables affecting grant distribution. Key findings highlight that educational levels and infrastructure significantly impact funding success. Recommendations include expanding data sources, conducting longitudinal studies, and enhancing policy evaluations to ensure equitable access to LAMP resources.

Introduction

Local Agriculture Market Program (LAMP)

The USDA’s Agricultural Marketing Service (AMS) administers a variety of grant programs aimed at strengthening local and regional food systems. The Local Agriculture Market Program (LAMP) is one such program that supports direct producer-to-consumer marketing, food enterprises, and value-added agricultural products. Established under the 2018 Farm Bill, LAMP fosters community collaboration and public-private partnerships to improve regional food economies, aiding in the development of business strategies and infrastructure for local food systems. The Farm Bill provided LAMP $50 million per year in mandatory funding and the programs received significant supplemental funding through the Consolidated Appropriations Act of 2021 and the American Rescue Plan of 2021.1 The major grant programs within LAMP include the Local Food Promotion Program (LFPP),2 Regional Food Systems Partnership (RFSP),3 and the Farmers Market Promotion Program (FMPP).4

Promotional materials for LAMP. Image: USDA-AMS, 2024

Building community capital through food systems investment

The goals of the LAMP program include: simplifying the application processes and the reporting processes for the Program; improving income and economic opportunities for producers and food businesses through job creation; and strengthening capacity and regional food system development through community collaboration and expansion of mid-tier value chains.5

USDA aims to make LAMP funding available to all eligible entities, with a particular emphasis on underserved communities. While the extent to which these aims are being met is not entire clear, AMS is working to better understand the impacts of these efforts. In 2021, for example, AMS partnered with Florida A&M University and the University of Maryland Eastern Shore on a project focusing on the following goals:6

  1. Evaluate barriers to AMS grant opportunities for socially disadvantaged communities

  2. Invest in building trust and confidence between these communities and the USDA

  3. Take action to rectify inequalities in program access through targeted outreach, training, and technical assistance.

The results of this work are intended to be used to improve access and reduce barriers for all applicants, presumably part of the agency’s renewed efforts to address USDA’s history of systemic discrimination.7

Community preparedness

Recent research suggests that the success of food system interventions, policies, and strategies for local economic development may hinge on the preexisting levels of community capital, as demonstrated by studies highlighting the pivotal role of social networks, trust, and local governance in achieving sustainable outcomes.8 9 10

Additional research showed positive associations between cultural and social capital and farm to school activity, indicating that communities with higher levels of social engagement and cultural assets are more likely to implement and sustain such programs successfully.11 12 13

Much of this research highlights community assets that are often overlooked in community development work, emphasizing the importance of recognizing and leveraging local strengths such as volunteer networks, informal social ties, and local knowledge systems for effective intervention.14 15 16

Objective

This report intends to lay the groundwork for an analytic approach that helps determine which community characteristics are associated with LAMP grant funding allocation. This could help determine if there is something akin to a “threshold of community preparedness” the unknowingly results in certain low-resource communities being excluded from LAMP programming. If so, the results of this research could provide insight into the particular characteristics associated with LAMP access, which could help agency staff to better allocate resources to ensure equitable access to grant funds.

Methods

Note

The code and data used to for the analysis and report generation can be accessed at https://github.com/elhohn/fas-capstone

Data access and aggregation

As a first step, a variety of potentially relevant data sets were obtained, cleaned, organized, and used for general data exploration. Information on specific datasets and sources can be found below. All work was done using the open source statistical software R version 4.4.0.17

LAMP grant data

Information on LAMP awards came from the LAMP Navigator website, where AMS has made this information publicly available, along with a dashboard for sorting, filtering, and visualizing the grant information.18 Along with information about the organizations receiving the grant, the available LAMP data includes information on the purpose of the grant (e.g., technical assistance, infrastructure, processing), the match amount, and the total project cost.

LAMP grant award amounts, 2006 - 2023
Each green dash represents a single grant award

For this analysis, the total award amount and the location of the organization receiving the grant were the key components used.

Geographic distribution of LAMP Grants, 2006-2023

Community characteristics

A variety of socioeconomic and environmental factors were investigated to assess how they may influence the likelihood of receiving a LAMP grant. These factors include indicators of community wealth, which encompasses social capital, natural capital, financial capital, and a variety of other forms of wealth, which have been shown impacts the ability to engage and participate in such programs.19 Additionally, it includes factors related to poverty and food security, which have been shown to exacerbate vulnerabilities and influence accessibility and participation in programs.20 Finally, considering the food system focus of LAMP, factors related to urbanization and proximity to agricultural land were included because they can influence market dynamics and food system connectivity.21

Indicators of community wealth

Community wealth data were accessed via the USDA AMS Data and Metrics GitHub repository.22 The main source of data was the “Indicators of Community Wealth” dataset stored within this repository, which is the output of various pre-processing steps that are outlined within the scripts found in the repository. Additional information about the variables used in this analysis can be found in the table below.

Table of indicators of community wealth variables and sources
Description Data Source
Demographics
racial_div Constructed racial diversity index from 0 (no diversity) to 10 (complete diversity), 2010 U.S. Census Bureau, Modified Race Data (2010)
insured Percent of population with health insurance Robert Wood Johnson Foundation, County Health Rankings
health_factors Health Factors Z-Score Robert Wood Johnson Foundation, County Health Rankings
health_outcomes Health Outcome Z-Score Robert Wood Johnson Foundation, County Health Rankings
Labor
create_jobs Percent of workforce employed in the arts USDA Economic Research Service, Creative Class Codes
Institutions
ed_attain Percent of adult population with at least a Bachelor's degree U.S. Census Bureau, American Community Survey, table S1501
Food Access
food_secure Percent of population food secure Feeding America Map the Meal Gap
Processing & Distribution
foodbev_est_CBP Food and beverage manufacturing establishments per 10,000 people U.S. Census Bureau, County Business Patterns
est_CBP Other manufacturing establishments per 10,000 people U.S. Census Bureau, County Business Patterns
Community Characteristics
highway_km Inverse of population-weighted distance (km) to nearest interstate highway ramp Dicken et al. (2011)
broad_16 Percent of population with access to fixed advanced telecomm FCC (2016)
pc1b_manufacturing Constructed index derived from a prinicipal component analysis including food and beverage establishments, and other manufacturing establishments Derived in Schmitt et al. (2021)
pc2b_infrastructure Constructed index derived from a prinicipal component analysis including percent of population with access to telecommunications, and proximity to highway ramp Derived in Schmitt et al. (2021)
create_indus Creative industry businesses per 100,000 population, 2014 Kushner & Cohen, Local Arts Index (2018)
pub_lib Public libraries per 100,000 people Kushner & Cohen, Local Arts Index (2018)
museums Museums per 100,000 people Kushner & Cohen, Local Arts Index (2018)
pc1c_artsdiversity Constructed index derived from a prinicipal component analysis including percent of workforce employed in the arts, and racial diversity index Derived in Schmitt et al. (2021)
pc2c_creativeindustries Constructed index derived from a prinicipal component analysis including public libraries per 100,000 people, creative industry businesses per 100,000 people, and museums per 100,000 people. Derived in Schmitt et al. (2021)
localgovfin Cash and security holdings less government debt per capita U.S. Census Bureau, Annual survey of state and local government finance. Historical data (formerly Special 60). File: “_IndFin_1967-2012”
owner_occupied Owner-occupied units without a mortgage per capita U.S. Census Bureau, American Community Survey, table S2507
deposits Bank deposits per capita at FDIC-insured institutions FDIC, Deposit Market Share Reports - Summary of Deposits
pc1f Financial capital - financial solvency Derived in Schmitt et al. (2021)
primary_care Number of primary care physicians per 10,000 population Robert Wood Johnson Foundation, County Health Rankings
pc1h_healtheducation Constructed index derived from a prinicipal component analysis of human capital data including educational attinment, health facor and outcome score from the Robert Wood Johnson Foundation Derived in Schmitt et al. (2021)
pc2h_medicalfoodsecurity Constructed index derived from a prinicipal component analysis of human capital data including percent of population food secure, percent of population with health insurance, and number of primary care phsyicans per 10,000. Derived in Schmitt et al. (2021)
natamen_scale Natural Amenities Scale McGranahan, D., 1999. Natural Amenities Scale. U.S. Department of Agriculture, Economic Research Service
prime_farmland Percent of farmland acres designated as prime farmland, 2012 U.S. Department of Agriculture, Natural Resource Conservation Service (USDA NRCS). 2012. National Resources Inventory
conserve_acre Percent of total acres with conservation easement, 2016 National Conservation Easement Database (NCED), 2016.
acre_FSA Percent of total acres in conservation-related programs and woodlands U.S. Department of Agriculture, Farm Service Agency (USDA FSA). 2017. FSA Crop Acreage Data
acre_NFS Percent of total acres in National Forests U.S. Forest Service (USFS). 2017. Land areas of the National Forest System. FS-383.
pc1n_naturalamenitiesconservation Constructed index derived from a prinicipal component analysis including natural amenity scale and share of acres in National Forest Derived in Schmitt et al. (2021)
pc2n_farmland Constructed index derived from a prinicipal component analysis including prime farmland Derived in Schmitt et al. (2021)
pvote Percent of eligible voters that voted Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates
nccs Number of nonprofit organizations per 1,000 population Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates
assn Number of social establishments per 1,000 population Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates
respn U.S. Population Census response rate, percent Rupasingha, Goetz, and Freshwater (2006) and 2017 data updates
pc1s_nonprofitsocialindustries Constructed index derived from a prinicipal component analysis including number of social establishments and nonprofits per capita Derived in Schmitt et al. (2021)
pc2s_publicvoiceparticipation Constructed index derived from a prinicipal component analysis including public voice and participation Derived in Schmitt et al. (2021)

Distribution of community wealth metrics

For each category listed in the table above, all metrics within that category were normalized, then the mean of all normalized values in the category was used to create an categorical percentile score. The colors in each category map layer below correspond to this percentile score. The values for the individual metrics can be seen by clicking on the county polygons.

Map of community wealth metrics

Additional community characteristics

In addition to community wealth indicators, a variety of demographic factors were incorporated into the analysis.

Poverty rate

Count-level poverty rates were sourced from the 2022 US Census.23 As an additional measurement of community capital, this information may supplement the indicators of community wealth data described above.

Farmland proportion

Data on total agricultural land per county was obtained from the 2022 Census of Agriculture.24 These area values were divided by the total area of each county to determine the percentage of total land in a given county that is dedicated to agricultural production. This information could provide insight into the importance of agriculture in each county, which may relate to food systems investment.

“Rural” and “underserved” classification

Data on county-level classification as “rural” and/or “underserved” were obtained from the Consumer Financial Protection Bureau.25 Because a focus of LAMP grants funding is underserved communities, this information may relate to LAMP funding across counties.

Rural-Urban Continuum Classification

The categorization of each county on the rural-urban continuum came from the USDA-ERS.26 This may be an important factor to consider, as research has shown relationships between rurality, capital stocks, and food systems participation.27

Exploratory map of additional explanatory variables

Analyzing the relationship between community characteristics and LAMP funding

The relationship between LAMP awards and community characteristics is evaluated in order to determine if there are significant gaps in the distribution of grant money across communities, and if such a gap is identified, what characteristics describe the communities within it. This information could help AMS determine what interventions may be necessary to fill these gaps.

LAMP award amounts vs. individual variables

As a first step in assessing this relationship, each of the community characteristics described above are plotted against the total per capita LAMP funding received. While the relationships at play are likely highly complex, with variables interacting with one another in nuanced ways, this initial step aims to isolate each variable in search of any clear patterns that may emerge.

In the plots below, each point represents a county. The x axis of each plot is the value for variable described in the sub-plot header (e.g., “acres in conservation”), and the y axis is the total per capita LAMP funding.

Per capita LAMP grants received vs community characteristics

While no conclusions can be drawn from the plots above, there are a number of interesting, if somewhat intuitive, trends that can be seen. For example, the rural continuum, where higher values indicate more rurality, shows an upward slope, indicating greater amounts of per capita funding going to counties that are more rural in nature. Similarly, the number of food and beverage industries per capita shows a positive relationship with LAMP funding, possibly a sign of relatively greater investment in food systems within those counties.

However, all of these relationship are speculative, and the degree, and direction, of causation cannot be determined through this analysis alone.

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of multivariate data sets. It aims to simplify the data by reducing the number of variables into a smaller number of components, while still retaining as much variation from the original variables as possible, thereby allowing for a more concise expression of a larger set of variables.

In the context of this analysis, PCA is thought to be an appropriate approach because of the thematic overlap among groups of variables, implying that multicolinearity may be an issue. Indeed, a correlation matrix of all variables shows a number of highly correlated variables within the dataset, where darker blue means higher positive correlation, and darker red indicates higher negative correlation.

Correlation matrix

After disposing of all principal components with eigenvalues less than one, ten principal components remained. As shown in the table below, these ten principal components cumulatively account for 70.4 percent of the variance in the data, while reducing the dimensionality by more than 75 percent.

Proportion of variance explained by the top 10 principal components
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10
Standard deviation 2.84 2.42 1.92 1.52 1.35 1.33 1.25 1.12 1.08 1.07
Proportion of Variance 20.1% 14.7% 9.2% 5.8% 4.6% 4.4% 3.9% 3.1% 2.9% 2.9%
Cumulative Proportion 20.1% 34.8% 44.0% 49.8% 54.4% 58.8% 62.7% 65.8% 68.7% 71.6%

Each of these principal components is composed of some pThe table and plots below show the main variables contributing to each of these components.

Discussion of retained principal components

The ten retained principal components were renamed according to their contributing variables. While not all have a clear categorical theme, the PCs were renamed as the following:

  • PC1 -> “Education”

  • PC2 -> “Arts”

  • PC3 - > “Conservation1”

  • PC4 -> “Farmland1”

  • PC5 -> “Infrastructure”

  • PC6 -> “Farmland2”

  • PC7 -> “Manufacturing”

  • PC8 -> “Conservation2”

  • PC9 -> “Food and Beverage”

  • PC10 -> “Civics”

The principal components are used later in this analysis as explanatory variables in a regression model.

Cluster analysis

Another approach to dealing with the large number of variables that may be related to LAMP grant funding is to use a clustering algorithm to find groups of counties that are similar across many variables.

The optimal number of clusters was determined using the “elbow method”, which works by running k-means clustering using a range of K values (1:20 was used in this analysis), calculating the average score for each cluster, and plotting the mean distance. The optimal value of K is the point where the rate of decrease in distortion or inertia shifts, known as the “elbow”.

“Elbow plot”

Cluster exploration

Principal Component #1 - Education

Principal Component #2 - Arts

Principal Component #3 - Conservation1

Principal Component #4 - Farmland1

Principal Component #5 - Infrastructure

Principal Component #6 - Farmland2

Principal Component #7 - Manufacturing

Principal Component #8 - Conservation2

Principal Component #9 - Food and Beverage

Principal Component #10 - Civics

Regression analysis

Regression analysis is a statistical technique that is used to estimate the effect of one or more explanatory variables on a dependent variable. In this case, regression can be used to evaluate the relationship between each of the community characteristic variables (the explanatory variables) and the LAMP funding amounts (the dependent variable) at the county level.

While each of the original original 44 community characteristic variables could be included in the regression model as explanatory variables, this analysis opts to use only the ten principal components. These components capture much of the variance of the original variables, while allowing for a simpler model that is easier to interpret.

Linear regression using per capita LAMP funding

For this analysis, a linear regression could be used to look at the relationship between community characteristics and the total amount of LAMP funding per county. This approach provides estimates on the relationship between each explanatory variable and the actual dollar amount going to each county.

Z-test of coefficients - linear regression
Estimate Std. Error z value Pr(>|z|)
(Intercept) 44744.60 6549.34 6.83 0***
pc1_education 15038.21 972.68 15.46 0***
pc2_arts 5894.41 883.75 6.67 0***
pc3_conservation1 -7661.01 1126.02 -6.80 0***
pc4_farmland1 -3000.95 1288.30 -2.33 0.02*
pc5_infrastructure 13271.32 1545.99 8.58 0***
pc6_farmland2 -3161.77 1538.94 -2.05 0.04*
pc7_manufacturing 10298.24 1489.88 6.91 0***
pc8_conservation2 -4663.01 1923.25 -2.42 0.02*
pc9_foodbev 5475.86 2091.80 2.62 0.01**
pc10_civics 1264.59 1930.54 0.66 0.51
RUCC_20232 34303.81 9045.69 3.79 0***
RUCC_20233 10734.02 9445.00 1.14 0.26
RUCC_20234 6788.08 9972.86 0.68 0.5
RUCC_20235 -18072.54 14147.46 -1.28 0.2
RUCC_20236 1029.48 9368.86 0.11 0.91
RUCC_20237 -13688.40 11244.76 -1.22 0.22
RUCC_20238 5723.09 9794.59 0.58 0.56
RUCC_20239 -10152.37 9935.52 -1.02 0.31
is_rural1 8562.10 7237.22 1.18 0.24
is_underserved1 -3215.16 4430.87 -0.73 0.47
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Logistic regression using binary funded/not funded

Alternatively, a logistic regression could be used to focus on whether or not a given county received any LAMP funding at all. This approach provides estimates on the relationship between each explanatory variable and the probability of a given county receiving any LAMP funding at all.

Z-test of coefficients - logistic regression
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.13 0.15 -7.37 0***
pc1_education 0.44 0.03 17.21 0***
pc2_arts 0.25 0.04 6.58 0***
pc3_conservation1 -0.16 0.03 -5.27 0***
pc4_farmland1 0.02 0.04 0.49 0.63
pc5_infrastructure 0.40 0.10 3.99 0***
pc6_farmland2 0.06 0.08 0.83 0.4
pc7_manufacturing 0.34 0.16 2.09 0.04*
pc8_conservation2 -0.07 0.05 -1.32 0.19
pc9_foodbev 0.13 0.08 1.54 0.12
pc10_civics 0.00 0.08 -0.05 0.96
RUCC_20232 0.42 0.14 3.01 0***
RUCC_20233 0.11 0.15 0.74 0.46
RUCC_20234 0.39 0.19 2.07 0.04*
RUCC_20235 0.24 0.30 0.80 0.43
RUCC_20236 0.00 0.21 0.01 0.99
RUCC_20237 -0.48 0.27 -1.76 0.08
RUCC_20238 0.24 0.25 0.97 0.33
RUCC_20239 -0.16 0.29 -0.55 0.58
is_rural1 -0.02 0.17 -0.14 0.89
is_underserved1 -0.63 0.31 -2.00 0.05*
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Discussion

This report provides an initial analysis of community characteristics influencing the allocation of LAMP grant funding. Through methods such as Principal Component Analysis and clustering, it identifies key factors and patterns that may affect grant distribution. The findings highlight potential barriers and suggest that certain community attributes, like educational levels and infrastructure, play significant roles in grant success. These insights can guide policy recommendations to ensure more equitable access to LAMP resources, ultimately fostering stronger local and regional food systems across diverse communities.

Next Steps and Future Research

  1. Expand Data Sources: Incorporate additional datasets, such as local economic indicators and community resilience metrics, to provide a more holistic view of factors influencing LAMP funding allocation.

  2. Longitudinal Studies: Conduct longitudinal studies to track changes in community characteristics over time and their impact on LAMP funding success. This can help identify long-term trends and effects of previous funding.

  3. Qualitative Research: Complement quantitative analysis with qualitative research, such as interviews and focus groups, to capture the nuanced experiences and challenges faced by communities in accessing LAMP grants.

  4. Policy Evaluation: Evaluate the effectiveness of current LAMP policies and outreach programs. This can involve assessing whether targeted interventions are reducing barriers for socially disadvantaged communities.

  5. Geospatial Analysis: Use advanced geospatial analysis to identify spatial patterns and clusters of grant allocation, focusing on underserved regions that may benefit from targeted support.

  6. Collaboration and Partnerships: Encourage collaboration between academic institutions, government agencies, and community organizations to share data, insights, and best practices, enhancing the overall impact of LAMP programs.

  7. Impact Assessment: Develop metrics to assess the socio-economic impact of LAMP funding on local food systems, including job creation, business growth, and community well-being, to better understand and communicate the program’s benefits.

Footnotes

  1. https://www.ams.usda.gov/sites/default/files/media/LAMP_Report_to_Congress.pdf↩︎

  2. https://www.ams.usda.gov/services/grants/lfpp↩︎

  3. https://www.ams.usda.gov/services/grants/rfsp↩︎

  4. https://www.ams.usda.gov/services/grants/fmpp↩︎

  5. https://www.ams.usda.gov/services/grants/lamp. Accessed April 20, 2024↩︎

  6. https://www.ams.usda.gov/sites/default/files/media/MSDUSDAAMSGrantApplicantTASociallyDisadvantaged.pdf↩︎

  7. Kashyap, Pratyoosh, Becca B.R. Jablonski, and Allison Bauman. “Exploring the Relationships among Stocks of Community Wealth, State Farm to School Policies, and the Intensity of Farm to School Activities.” Food Policy 122 (January 2024): 102570. https://doi.org/10.1016/j.foodpol.2023.102570.↩︎

  8. Schmit, Todd M., Becca B.R. Jablonski, Alessandro Bonanno, and Thomas G. Johnson. “Measuring Stocks of Community Wealth and Their Association with Food Systems Efforts in Rural and Urban Places.” Food Policy 102 (July 2021): 102119. https://doi.org/10.1016/j.foodpol.2021.102119.↩︎

  9. Flora, Cornelia Butler, Jan L. Flora, and Stephen P. Gasteyer. Rural Communities: Legacy and Change. 4th ed. Routledge, 2018. https://doi.org/10.4324/9780429494697.↩︎

  10. Emery, Mary, and Cornelia Flora. “Spiraling-Up: Mapping Community Transformation with Community Capitals Framework.” Community Development 37, no. 1 (March 2006): 19–35. https://doi.org/10.1080/15575330609490152.↩︎

  11. Kashyap, Pratyoosh, Becca B.R. Jablonski, and Allison Bauman. “Exploring the Relationships among Stocks of Community Wealth, State Farm to School Policies, and the Intensity of Farm to School Activities.” Food Policy 122 (January 2024): 102570. https://doi.org/10.1016/j.foodpol.2023.102570.↩︎

  12. Kaiser, T., & Schneickert, C. (2016). Cultural Participation, Personality and Educational Inequalities. Sociological Research Online, 21(3), 41-56. https://doi.org/10.5153/sro.4063↩︎

  13. Bagdonis, Jessica M., C. Clare Hinrichs, and Kai A. Schafft. “The emergence and framing of farm-to-school initiatives: Civic engagement, health and local agriculture.” Agriculture and Human Values 26 (2009): 107-119.↩︎

  14. Kashyap, Pratyoosh, Becca B.R. Jablonski, and Allison Bauman. “Exploring the Relationships among Stocks of Community Wealth, State Farm to School Policies, and the Intensity of Farm to School Activities.” Food Policy 122 (January 2024): 102570. https://doi.org/10.1016/j.foodpol.2023.102570.↩︎

  15. Pender, John, Alexander Marré, and Richard Reeder. “Rural wealth creation concepts, strategies, and measures.” USDA-ERS Economic Research Report 131 (2012).↩︎

  16. Green, Gary Paul, and Anna Haines. Asset building & community development. Sage publications, 2015.↩︎

  17. https://www.r-project.org/↩︎

  18. https://www.ams.usda.gov/data/lamp-navigator↩︎

  19. Flora, Cornelia Butler, Jan L. Flora, and Stephen P. Gasteyer. Rural Communities: Legacy and Change. 4th ed. Routledge, 2018. https://doi.org/10.4324/9780429494697.↩︎

  20. Alisha Coleman-Jensen, Matthew P. Rabbitt, Christian A. Gregory, and Anita Singh. 2021. Household Food Security in the United States in 2020, ERR-298, U.S. Department of Agriculture, Economic Research Service.↩︎

  21. Pothukuchi, Kameshwari, and Jerome L. Kaufman. “The Food System: A Stranger to the Planning Field.” Journal of the American Planning Association 66, no. 2 (June 30, 2000): 113–24. https://doi.org/10.1080/01944360008976093.↩︎

  22. https://github.com/CSU-Local-and-Regional-Food-Systems/USDA-AMS-Data-and-Metrics/tree/main↩︎

  23. U.S. Census Bureau. (n.d.). SAIPE State and County Estimates for 2022. Retrieved March 27, 2024, from https://data.census.gov/↩︎

  24. 2022 Census of Agriculture. QuickStats, State and County Data. [Washington, D.C.] :United States Department of Agriculture, National Agricultural Statistics Service, 2014.↩︎

  25. https://www.consumerfinance.gov/rural-or-underserved-tool/↩︎

  26. https://www.ers.usda.gov/data-products/rural-urban-continuum-codes/↩︎

  27. Schmit, Todd M., Becca BR Jablonski, Alessandro Bonanno, and Thomas G. Johnson. “Measuring stocks of community wealth and their association with food systems efforts in rural and urban places.” Food Policy 102 (2021): 102119.↩︎